Early Career Spotlights

Abstract:
The ultimate promise of robotics is to design devices that can
physically interact with the world. To date, robots have been
primarily deployed in highly structured and predictable
environments. However, we envision the next generation of robots
(ranging from self-driving and -flying vehicles to robot assistants)
to operate in unpredictable and generally unknown environments
alongside humans. This challenges current robot algorithms, which
have been largely based on a-priori knowledge about the system and
its environment. While research has shown that robots are able to
learn new skills from experience and adapt to unknown situations,
these results have been mostly limited to learning single tasks, and
demonstrated in simulation or lab settings. The next challenge is to
enable robot learning in real-world application scenarios. This will
require versatile, data-efficient and online learning algorithms
that guarantee safety when placed in a closed-loop system
architecture. It will also require to answer the fundamental
question of how to design learning architectures for dynamic and
interactive agents. This talk will highlight our recent progress in
combining learning methods with formal results from control
theory. By combining models with data, our algorithms achieve
adaptation to changing conditions during long-term operation,
data-efficient multi-robot, multi-task transfer learning, and safe
reinforcement learning. We demonstrate our algorithms in
vision-based off-road driving and drone flight experiments, as well
as on mobile manipulators.

Biography:
Angela Schoellig is an Assistant Professor at the University of
Toronto Institute for Aerospace Studies and an Associate Director of
the Centre for Aerial Robotics Research and Education. She holds a
Canada Research Chair in Machine Learning for Robotics and Control,
is a principal investigator of the NSERC Canadian Robotics Network,
and is a Faculty Affiliate of the Vector Institute for Artificial
Intelligence. She conducts research at the intersection of robotics,
controls, and machine learning. Her goal is to enhance the
performance, safety, and autonomy of robots by enabling them to
learn from past experiments and from each other. She is a recipient
of a Sloan Research Fellowship (2017), an Ontario Early Researcher
Award (2017), and a Connaught New Researcher Award (2015). She is
one of MIT Technology Review’s Innovators Under 35 (2017), a Canada
Science Leadership Program Fellow (2014), and one of Robohub’s “25
women in robotics you need to know about (2013)”. Her team won the
2018 North-American SAE AutoDrive Challenge sponsored by General
Motors. Her PhD at ETH Zurich (2013) was awarded the ETH Medal and
the Dimitris N. Chorafas Foundation Award. She holds both an
M.Sc. in Engineering Cybernetics from the University of Stuttgart
(2008) and an M.Sc. in Engineering Science and Mechanics from the
Georgia Institute of Technology (2007). More information can be
found at: www.schoellig.name

Abstract:
In this talk I will present a decision-making and control stack for
human-robot interactions by using autonomous driving as a motivating
example. Specifically, I will first discuss a data-driven approach
for learning multimodal interaction dynamics between robot-driven
and human-driven vehicles based on recent advances in deep
generative modeling. Then, I will discuss how to incorporate such a
learned interaction model into a real-time, interaction-aware
decision-making framework. The framework is designed to be minimally
interventional; in particular, by leveraging backward reachability
analysis, it ensures safety even when other cars defy the robot's
expectations without unduly sacrificing performance. I will present
recent results from experiments on a full-scale steer-by-wire
platform, validating the framework and providing practical
insights. I will conclude the talk by providing an overview of
related efforts from my group on infusing safety assurances in robot
autonomy stacks equipped with learning-based components, with an
emphasis on adding structure within robot learning via
control-theoretical and formal methods.

Biography:
Dr. Marco Pavone is an Associate Professor of Aeronautics and
Astronautics at Stanford University, where he is the Director of the
Autonomous Systems Laboratory and Co-Director of the Center for
Automotive Research at Stanford. Before joining Stanford, he was a
Research Technologist within the Robotics Section at the NASA Jet
Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and
Astronautics from the Massachusetts Institute of Technology in
2010. His main research interests are in the development of
methodologies for the analysis, design, and control of autonomous
systems, with an emphasis on self-driving cars, autonomous aerospace
vehicles, and future mobility systems. He is a recipient of a number
of awards, including a Presidential Early Career Award for
Scientists and Engineers from President Barack Obama, an Office of
Naval Research Young Investigator Award, a National Science
Foundation Early Career (CAREER) Award, and a NASA Early Career
Faculty Award. He was identified by the American Society for
Engineering Education (ASEE) as one of America's 20 most highly
promising investigators under the age of 40. His work has been
recognized with best paper nominations or awards at the IEEE
International Conference on Intelligent Transportation Systems, at
the Field and Service Robotics Conference, at the Robotics: Science
and Systems Conference, at the ROBOCOMM Conference, and at NASA
symposia. He is currently serving as an Associate Editor for the
IEEE Control Systems Magazine.